scripts/supplementary.R
In GrosseLab/BGSC: Bayesian gene selection criterion (BGSC) approach
plotOUT <- './PaperPlot/'
dodge <- position_dodge(width=0.9)
base_size <- 20
# PlotsPaper mRNA G0G1 Proliferation -------------------------------------
ExcelFiles <- c("./rawdata/supplementary/RNA_Daten_zur_SF767_CCND1.csv",
"./rawdata/supplementary/RNA_Daten_zur_SF767_EGFRall.csv",
"./rawdata/supplementary/RNA_Daten_zur_SF767_EGFRv1.csv",
"./rawdata/supplementary/RNA_Daten_zur_SF767_EGFRv4.csv",
"./rawdata/supplementary/RNA_Daten_zur_SF767_G0G1.csv",
"./rawdata/supplementary/RNA_Daten_zur_SF767_GAPDH.csv",
"./rawdata/supplementary/RNA_Daten_zur_SF767_MMP2.csv",
"./rawdata/supplementary/RNA_Daten_zur_SF767_proliferation48H.csv",
"./rawdata/supplementary/RNA_Daten_zur_SF767_SVV.csv")
ExcelFilesNames <- c("CCND1","EGFRall","EGFRv1","EGFRv4","G0G1","GAPDH","MMP2","proliferation48H","SVV")
names(ExcelFiles) <- ExcelFilesNames
ExcelFilesList <- lapply(ExcelFilesNames,function(x) read.csv(ExcelFiles[x],sep = ';',dec = ',') )
names(ExcelFilesList) <- ExcelFilesNames
OutNames <- c("CCND1","EGFR variants I-IV ","EGFR variant I","EGFR variant IV","G0G1","GAPDH","MMP2","proliferation48H","BIRC5")
names(OutNames) <- ExcelFilesNames
# PlotsPaper mRNA -------------------------------------
mRNANames <- c("EGFRall","EGFRv1","EGFRv4","GAPDH","MMP2","SVV","CCND1")
PData <- data.frame()
for(mRNA in mRNANames){
mat <- matrix(as.double(unlist(ExcelFilesList[[mRNA]][,c(5:8)])),4,4)
dimnames(mat) <- list(as.character(ExcelFilesList[[mRNA]][,'Behandlung']),c('rep1','rep2','rep3','rep4'))
# rownames(mat)[2] <- 'siRNA-luci'
rownames(mat)[2] <- 'siRNA-nonsense'
print(mat)
pdata <- data.frame( "Treat" = rownames(mat),
"Gene" = mRNA,
"Out" = OutNames[mRNA],
"GeoMean" = (2^rowMeans(log2(mat),na.rm = T )),
"Mean" = rowMeans(mat,na.rm = T),
"stderr" = apply(mat,1,function(row) plotrix::std.error((row),na.rm = T)),
"sd" = apply(mat,1,function(row) sd((row),na.rm = T)))
PData <- rbind(PData,pdata)
rns <- levels(pdata$Treat)[c(1,4,2,3)] ; pdata$Treat <- factor(pdata$Treat, levels = rns)
limitsMeanErr <- aes(ymax = pdata$Mean + pdata$stderr , ymin=pdata$Mean - pdata$stderr) ; limitsMeanErrMAX <- round(max(pdata$Mean + pdata$stderr)+10)
limitsGeoMeanErr <- aes(ymax = pdata$GeoMean + pdata$stderr , ymin=pdata$GeoMean - pdata$stderr); limitsGeoMeanErrMAX <- round(max(pdata$GeoMean + pdata$stderr)+10)
limitsMeanSd <- aes(ymax = pdata$Mean + pdata$sd , ymin=pdata$Mean - pdata$sd); limitsMeanSdMAX <- round(max(pdata$Mean + pdata$sd)+10)
limitsGeoMeanSd <- aes(ymax = pdata$GeoMean + pdata$sd , ymin=pdata$GeoMean - pdata$sd); limitsGeoMeanSdMAX <- round(max(pdata$GeoMean + pdata$sd)+10)
PlotMeanSd <- ggplot(pdata,aes(x=factor(Treat),y=Mean)) +
geom_bar(position="dodge",stat="identity") +
geom_errorbar(limitsMeanSd, position=dodge, width=0.4) +
#ylim(c(0 ,limitsMeanSdMAX)) +
labs(x = "", y = paste0("relative mRNA level of ",OutNames[mRNA]," copies / \n HPRT copies"))+ # ,title="PAPER PLOT - Comparison of SF767 - Illumina data and RT-qPCR") +
thememapBarplot(base_size = base_size,legend_key_size = 0.6) +
scale_y_continuous(breaks=seq(0,limitsMeanSdMAX,20))
#theme(legend.position="bottom")
# scale_fill_manual(values = cols,name="") +
print(PlotMeanSd)
ggsave(plot = PlotMeanSd,filename = paste0(plotOUT,'mRNA_',mRNA,'_MeanSd.pdf'),width = 12,height = 8)
# PlotMeanErr <- ggplot(pdata,aes(x=factor(Treat),y=Mean)) +
# geom_bar(position="dodge",stat="identity") +
# geom_errorbar(limitsMeanErr, position=dodge, width=0.4) +
# # ylim(c(0 ,limitsMeanErrMAX)) +
# labs(x = "", y = paste0("relative mRNA level of ",OutNames[mRNA]," copies / \n HPRT copies"))+ # ,title="PAPER PLOT - Comparison of SF767 - Illumina data and RT-qPCR") +
# thememapBarplot(base_size = base_size,legend_key_size = 0.6) +
# scale_y_continuous(breaks=seq(0,limitsMeanErrMAX,20))
# #theme(legend.position="bottom")
# # scale_fill_manual(values = cols,name="") +
# print(PlotMeanErr)
# ggsave(plot = PlotMeanErr,filename = paste0(plotOUT,'mRNA_',mRNA,'_MeanErr.pdf'),width = 12,height = 8)
#
# PlotGeoMeanSd <- ggplot(pdata,aes(x=factor(Treat),y=GeoMean)) +
# geom_bar(position="dodge",stat="identity") +
# geom_errorbar(limitsGeoMeanSd, position=dodge, width=0.4) +
# #ylim(c(0 ,limitsGeoMeanSdMAX)) +
# labs(x = "", y = paste0("relative mRNA level of ",OutNames[mRNA]," copies / \n HPRT copies"))+ # ,title="PAPER PLOT - Comparison of SF767 - Illumina data and RT-qPCR") +
# thememapBarplot(base_size = base_size,legend_key_size = 0.6) +
# scale_y_continuous(breaks=seq(0,limitsGeoMeanSdMAX,20))
# #theme(legend.position="bottom")
# # scale_fill_manual(values = cols,name="") +
# print(PlotGeoMeanSd)
# ggsave(plot = PlotGeoMeanSd,filename = paste0(plotOUT,'mRNA_',mRNA,'_GeoMeanSd.pdf'),width = 12,height = 8)
#
# PlotGeoMeanErr <- ggplot(pdata,aes(x=factor(Treat),y=GeoMean)) +
# geom_bar(position="dodge",stat="identity") +
# geom_errorbar(limitsGeoMeanErr, position=dodge, width=0.4) +
# #ylim(c(0 ,limitsGeoMeanErrMAX)) +
# labs(x = "", y = paste0("relative mRNA level of ",OutNames[mRNA]," copies / \n HPRT copies"))+ # ,title="PAPER PLOT - Comparison of SF767 - Illumina data and RT-qPCR") +
# thememapBarplot(base_size = base_size,legend_key_size = 0.6) +
# scale_y_continuous(breaks=seq(0,limitsGeoMeanErrMAX,20))
# #theme(legend.position="bottom")
# # scale_fill_manual(values = cols,name="") +
# print(PlotGeoMeanErr)
# ggsave(plot = PlotGeoMeanErr,filename = paste0(plotOUT,'mRNA_',mRNA,'_GeoMeanErr.pdf'),width = 12,height = 8)
}
rns <- levels(PData$Treat)[c(1,4,2,3)] ; PData$Treat <- factor(PData$Treat, levels = rns)
limitsMeanErr <- aes(ymax = PData$Mean + PData$stderr , ymin=PData$Mean - PData$stderr) ; limitsMeanErrMAX <- round(max(PData$Mean + PData$stderr)+10)
limitsGeoMeanErr <- aes(ymax = PData$GeoMean + PData$stderr , ymin=PData$GeoMean - PData$stderr); limitsGeoMeanErrMAX <- round(max(PData$GeoMean + PData$stderr)+10)
limitsMeanSd <- aes(ymax = PData$Mean + PData$sd , ymin=PData$Mean - PData$sd); limitsMeanSdMAX <- round(max(PData$Mean + PData$sd)+10)
limitsGeoMeanSd <- aes(ymax = PData$GeoMean + PData$sd , ymin=PData$GeoMean - PData$sd); limitsGeoMeanSdMAX <- round(max(PData$GeoMean + PData$sd)+10)
PlotMeanSd <- ggplot(PData,aes(x=factor(Treat),y=Mean)) +
geom_bar(position="dodge",stat="identity") +
geom_errorbar(limitsMeanSd, position=dodge, width=0.4) +
#ylim(c(0 ,limitsMeanSdMAX)) +
labs(x = "", y = paste0("relative mRNA level of gene copies / HPRT copies")) +
thememapBarplot(base_size = base_size,legend_key_size = 0.6) +
scale_y_continuous(breaks=seq(0,limitsMeanSdMAX,20)) +
facet_grid(Out ~ .)
#theme(legend.position="bottom")
# scale_fill_manual(values = cols,name="") +
print(PlotMeanSd)
ggsave(plot = PlotMeanSd,filename = paste0(plotOUT,'mRNA_ALL_MeanSd.pdf'),width = 12,height = 20)
barCol <- RColorBrewer::brewer.pal(11,'Spectral')[6]
for(i in c("EGFR",'Control','Proli','EGFRControl')){
set <- switch(i,
"EGFR" = as.character( levels(PData$Gene)[1:3] ),
'Control' = as.character( levels(PData$Gene)[4:5] ),
'Proli' = as.character( levels(PData$Gene)[6:7] ),
'EGFRControl'= as.character( levels(PData$Gene)[1:5] )
)
tmp <- PData[PData$Gene %in% set,]
tmp_limitsMeanSd <- aes(ymax = tmp$Mean + tmp$sd , ymin=tmp$Mean - tmp$sd); limitsMeanSdMAX <- round(max(tmp$Mean + tmp$sd)+10)
PlotMeanSd <- ggplot(tmp,aes(x=factor(Treat),y=Mean)) +
geom_bar(position="dodge",stat="identity",fill=barCol,color='black') +
geom_errorbar(tmp_limitsMeanSd, position=dodge, width=0.4) +
labs(x = "", y = paste0("relative mRNA level of gene copies / HPRT copies")) +
thememapBarplot(base_size = base_size,legend_key_size = 0.6) +
scale_y_continuous(breaks=seq(0,limitsMeanSdMAX,20)) +
facet_grid(Out ~ .)
print(PlotMeanSd)
ggsave(plot = PlotMeanSd,filename = paste0(plotOUT,'mRNA_ALL_MeanSd_',i,'.pdf'),width = 8,height = 15)
}
# PlotMeanErr <- ggplot(PData,aes(x=factor(Treat),y=Mean)) +
# geom_bar(position="dodge",stat="identity") +
# geom_errorbar(limitsMeanErr, position=dodge, width=0.4) +
# # ylim(c(0 ,limitsMeanErrMAX)) +
# labs(x = "", y = paste0("relative mRNA level of gene copies / HPRT copies")) +
# thememapBarplot(base_size = base_size,legend_key_size = 0.6) +
# scale_y_continuous(breaks=seq(0,limitsMeanErrMAX,20)) +
# facet_grid(Out ~ .)
# #theme(legend.position="bottom")
# # scale_fill_manual(values = cols,name="") +
# print(PlotMeanErr)
# ggsave(plot = PlotMeanErr,filename = paste0(plotOUT,'mRNA_ALL_MeanErr.pdf'),width = 12,height = 20)
#
# PlotGeoMeanSd <- ggplot(PData,aes(x=factor(Treat),y=GeoMean)) +
# geom_bar(position="dodge",stat="identity") +
# geom_errorbar(limitsGeoMeanSd, position=dodge, width=0.4) +
# #ylim(c(0 ,limitsGeoMeanSdMAX)) +
# labs(x = "", y = paste0("relative mRNA level of gene copies / HPRT copies")) +
# thememapBarplot(base_size = base_size,legend_key_size = 0.6) +
# scale_y_continuous(breaks=seq(0,limitsGeoMeanSdMAX,20)) +
# facet_grid(Out ~ .)
# #theme(legend.position="bottom")
# # scale_fill_manual(values = cols,name="") +
# print(PlotGeoMeanSd)
# ggsave(plot = PlotGeoMeanSd,filename = paste0(plotOUT,'mRNA_ALL_GeoMeanSd.pdf'),width = 12,height = 20)
#
# PlotGeoMeanErr <- ggplot(PData,aes(x=factor(Treat),y=GeoMean)) +
# geom_bar(position="dodge",stat="identity") +
# geom_errorbar(limitsGeoMeanErr, position=dodge, width=0.4) +
# #ylim(c(0 ,limitsGeoMeanErrMAX)) +
# labs(x = "", y = paste0("relative mRNA level of gene copies / HPRT copies")) +
# thememapBarplot(base_size = base_size,legend_key_size = 0.6) +
# scale_y_continuous(breaks=seq(0,limitsGeoMeanErrMAX,20)) +
# facet_grid(Out ~ .)
# #theme(legend.position="bottom")
# # scale_fill_manual(values = cols,name="") +
# print(PlotGeoMeanErr)
# ggsave(plot = PlotGeoMeanErr,filename = paste0(plotOUT,'mRNA_ALL_GeoMeanErr.pdf'),width = 12,height = 20)
# PlotsPaper proliferation48H -------------------------------------
set <- "proliferation48H"
mat <- matrix(as.double(unlist(ExcelFilesList[[set]][,c(3:7)])),10,5)
dimnames(mat) <- list(as.character(ExcelFilesList[[set]][,'Behandlung']),c('rep1','rep2','rep3','rep4','rep5'))
mat <- mat[c("K","Lu","All",'Full'),]
# rownames(mat) <- c('control','siRNA-luci','siRNA-ALL','siRNA-I')
rownames(mat) <- c('control','siRNA-nonsense','siRNA-ALL','siRNA-I')
pdata <- data.frame( "Treat" = rownames(mat),
# "Out" = OutNames[mRNA],
"GeoMean" = (2^rowMeans(log2(mat),na.rm = T )),
"Mean" = rowMeans(mat,na.rm = T),
"stderr" = apply(mat,1,function(row) plotrix::std.error((row),na.rm = T)),
"sd" = apply(mat,1,function(row) sd((row),na.rm = T)))
rns <- levels(pdata$Treat)[c(1,4,2,3)] ; pdata$Treat <- factor(pdata$Treat, levels = rns)
limitsMeanErr <- aes(ymax = pdata$Mean + pdata$stderr , ymin=pdata$Mean - pdata$stderr) ; limitsMeanErrMAX <- round(max(pdata$Mean + pdata$stderr)+10)
limitsGeoMeanErr <- aes(ymax = pdata$GeoMean + pdata$stderr , ymin=pdata$GeoMean - pdata$stderr); limitsGeoMeanErrMAX <- round(max(pdata$GeoMean + pdata$stderr)+10)
limitsMeanSd <- aes(ymax = pdata$Mean + pdata$sd , ymin=pdata$Mean - pdata$sd); limitsMeanSdMAX <- round(max(pdata$Mean + pdata$sd)+10)
limitsGeoMeanSd <- aes(ymax = pdata$GeoMean + pdata$sd , ymin=pdata$GeoMean - pdata$sd); limitsGeoMeanSdMAX <- round(max(pdata$GeoMean + pdata$sd)+10)
PlotMeanSd <- ggplot(pdata,aes(x=factor(Treat),y=Mean)) +
geom_bar(position="dodge",stat="identity",fill=barCol,color='black') +
geom_errorbar(limitsMeanSd, position=dodge, width=0.4) +
ylim(c(0 ,limitsMeanSdMAX)) +
# scale_y_continuous(breaks=seq(0,limitsMeanSdMAX,20)) +
labs(x = "", y = paste0("cell number after 48 hours \n of proliferation"))+ # ,title="PAPER PLOT - Comparison of SF767 - Illumina data and RT-qPCR") +
thememapBarplot(base_size = base_size,legend_key_size = 0.6)
#theme(legend.position="bottom")
# scale_fill_manual(values = cols,name="") +
print(PlotMeanSd)
ggsave(plot = PlotMeanSd,filename = paste0(plotOUT,'proliferation48H_MeanSd.pdf'),width = 12,height = 8)
# PlotMeanErr <- ggplot(pdata,aes(x=factor(Treat),y=Mean)) +
# geom_bar(position="dodge",stat="identity") +
# geom_errorbar(limitsMeanErr, position=dodge, width=0.4) +
# ylim(c(0 ,limitsMeanErrMAX)) +
# labs(x = "", y = paste0("cell number after 48 hours of proliferation"))+ # ,title="PAPER PLOT - Comparison of SF767 - Illumina data and RT-qPCR") +
# thememapBarplot(base_size = base_size,legend_key_size = 0.6)
# #theme(legend.position="bottom")
# # scale_fill_manual(values = cols,name="") +
# print(PlotMeanErr)
# ggsave(plot = PlotMeanErr,filename = paste0(plotOUT,'proliferation48H_MeanErr.pdf'),width = 12,height = 8)
#
# PlotGeoMeanSd <- ggplot(pdata,aes(x=factor(Treat),y=GeoMean)) +
# geom_bar(position="dodge",stat="identity") +
# geom_errorbar(limitsGeoMeanSd, position=dodge, width=0.4) +
# ylim(c(0 ,limitsGeoMeanSdMAX)) +
# labs(x = "", y = paste0("cell number after 48 hours of proliferation"))+ # ,title="PAPER PLOT - Comparison of SF767 - Illumina data and RT-qPCR") +
# thememapBarplot(base_size = base_size,legend_key_size = 0.6)
# #theme(legend.position="bottom")
# # scale_fill_manual(values = cols,name="") +
# print(PlotGeoMeanSd)
# ggsave(plot = PlotGeoMeanSd,filename = paste0(plotOUT,'proliferation48H_GeoMeanSd.pdf'),width = 12,height = 8)
#
# PlotGeoMeanErr <- ggplot(pdata,aes(x=factor(Treat),y=GeoMean)) +
# geom_bar(position="dodge",stat="identity") +
# geom_errorbar(limitsGeoMeanErr, position=dodge, width=0.4) +
# ylim(c(0 ,limitsGeoMeanErrMAX)) +
# labs(x = "", y = paste0("cell number after 48 hours of proliferation"))+ # ,title="PAPER PLOT - Comparison of SF767 - Illumina data and RT-qPCR") +
# thememapBarplot(base_size = base_size,legend_key_size = 0.6)
# #theme(legend.position="bottom")
# # scale_fill_manual(values = cols,name="") +
# print(PlotGeoMeanErr)
# ggsave(plot = PlotGeoMeanErr,filename = paste0(plotOUT,'proliferation48H_GeoMeanErr.pdf'),width = 12,height = 8)
# PlotsPaper G0G1 -------------------------------------
set <- "G0G1"
mat <- matrix(as.double(unlist(ExcelFilesList[[set]][,c(3:8)])),10,6)
dimnames(mat) <- list(as.character(ExcelFilesList[[set]][,'Bezeichnung']),c('rep1','rep2','rep3','rep4','rep5','rep6'))
mat <- mat[c("Ko","LU","All",'Full'),]
rownames(mat) <- c('control','siRNA-luci','siRNA-ALL','siRNA-I')
rownames(mat) <- c('control','siRNA-nonsense','siRNA-ALL','siRNA-I')
pdata <- data.frame( "Treat" = rownames(mat),
# "Out" = OutNames[mRNA],
"GeoMean" = (2^rowMeans(log2(mat),na.rm = T )),
"Mean" = rowMeans(mat,na.rm = T),
"stderr" = apply(mat,1,function(row) plotrix::std.error((row),na.rm = T)),
"sd" = apply(mat,1,function(row) sd((row),na.rm = T)))
rns <- levels(pdata$Treat)[c(1,4,2,3)] ; pdata$Treat <- factor(pdata$Treat, levels = rns)
limitsMeanErr <- aes(ymax = pdata$Mean + pdata$stderr , ymin=pdata$Mean - pdata$stderr) ; limitsMeanErrMAX <- round(max(pdata$Mean + pdata$stderr)+10)
limitsGeoMeanErr <- aes(ymax = pdata$GeoMean + pdata$stderr , ymin=pdata$GeoMean - pdata$stderr); limitsGeoMeanErrMAX <- round(max(pdata$GeoMean + pdata$stderr)+10)
limitsMeanSd <- aes(ymax = pdata$Mean + pdata$sd , ymin=pdata$Mean - pdata$sd); limitsMeanSdMAX <- round(max(pdata$Mean + pdata$sd)+10)
limitsGeoMeanSd <- aes(ymax = pdata$GeoMean + pdata$sd , ymin=pdata$GeoMean - pdata$sd); limitsGeoMeanSdMAX <- round(max(pdata$GeoMean + pdata$sd)+10)
PlotMeanSd <- ggplot(pdata,aes(x=factor(Treat),y=Mean)) +
geom_bar(position="dodge",stat="identity",fill=barCol,color='black') +
geom_errorbar(limitsMeanSd, position=dodge, width=0.4) +
# ylim(c(0 ,limitsMeanSdMAX)) +
scale_y_continuous(breaks=seq(0,limitsMeanSdMAX,10)) +
labs(x = "", y = paste0("relative level of G0/G1 cells"))+ # ,title="PAPER PLOT - Comparison of SF767 - Illumina data and RT-qPCR") +
thememapBarplot(base_size = base_size,legend_key_size = 0.6)
print(PlotMeanSd)
ggsave(plot = PlotMeanSd,filename = paste0(plotOUT,'G0G1_MeanSd.pdf'),width = 12,height = 8)
# PlotMeanErr <- ggplot(pdata,aes(x=factor(Treat),y=Mean)) +
# geom_bar(position="dodge",stat="identity") +
# geom_errorbar(limitsMeanErr, position=dodge, width=0.4) +
# scale_y_continuous(breaks=seq(0,limitsMeanErrMAX,10)) +
# labs(x = "", y = paste0("relative level of G0/G1 cells"))+ # ,title="PAPER PLOT - Comparison of SF767 - Illumina data and RT-qPCR") +
# thememapBarplot(base_size = base_size,legend_key_size = 0.6)
# #theme(legend.position="bottom")
# # scale_fill_manual(values = cols,name="") +
# print(PlotMeanErr)
# ggsave(plot = PlotMeanErr,filename = paste0(plotOUT,'G0G1_MeanErr.pdf'),width = 12,height = 8)
#
# PlotGeoMeanSd <- ggplot(pdata,aes(x=factor(Treat),y=GeoMean)) +
# geom_bar(position="dodge",stat="identity") +
# geom_errorbar(limitsGeoMeanSd, position=dodge, width=0.4) +
# scale_y_continuous(breaks=seq(0,limitsGeoMeanSdMAX,10)) +
# labs(x = "", y = paste0("relative level of G0/G1 cells"))+ # ,title="PAPER PLOT - Comparison of SF767 - Illumina data and RT-qPCR") +
# thememapBarplot(base_size = base_size,legend_key_size = 0.6)
# #theme(legend.position="bottom")
# # scale_fill_manual(values = cols,name="") +
# print(PlotGeoMeanSd)
# ggsave(plot = PlotGeoMeanSd,filename = paste0(plotOUT,'G0G1_GeoMeanSd.pdf'),width = 12,height = 8)
#
# PlotGeoMeanErr <- ggplot(pdata,aes(x=factor(Treat),y=GeoMean)) +
# geom_bar(position="dodge",stat="identity") +
# geom_errorbar(limitsGeoMeanErr, position=dodge, width=0.4) +
# scale_y_continuous(breaks=seq(0,limitsGeoMeanErrMAX,10)) +
# labs(x = "", y = paste0("relative level of G0/G1 cells"))+ # ,title="PAPER PLOT - Comparison of SF767 - Illumina data and RT-qPCR") +
# thememapBarplot(base_size = base_size,legend_key_size = 0.6)
# #theme(legend.position="bottom")
# # scale_fill_manual(values = cols,name="") +
# print(PlotGeoMeanErr)
# ggsave(plot = PlotGeoMeanErr,filename = paste0(plotOUT,'G0G1_GeoMeanErr.pdf'),width = 12,height = 8)
GrosseLab/BGSC documentation built on Sept. 4, 2019, 2:36 p.m.
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plotOUT <- './PaperPlot/'
dodge <- position_dodge(width=0.9)
base_size <- 20
# PlotsPaper mRNA G0G1 Proliferation -------------------------------------
ExcelFiles <- c("./rawdata/supplementary/RNA_Daten_zur_SF767_CCND1.csv",
"./rawdata/supplementary/RNA_Daten_zur_SF767_EGFRall.csv",
"./rawdata/supplementary/RNA_Daten_zur_SF767_EGFRv1.csv",
"./rawdata/supplementary/RNA_Daten_zur_SF767_EGFRv4.csv",
"./rawdata/supplementary/RNA_Daten_zur_SF767_G0G1.csv",
"./rawdata/supplementary/RNA_Daten_zur_SF767_GAPDH.csv",
"./rawdata/supplementary/RNA_Daten_zur_SF767_MMP2.csv",
"./rawdata/supplementary/RNA_Daten_zur_SF767_proliferation48H.csv",
"./rawdata/supplementary/RNA_Daten_zur_SF767_SVV.csv")
ExcelFilesNames <- c("CCND1","EGFRall","EGFRv1","EGFRv4","G0G1","GAPDH","MMP2","proliferation48H","SVV")
names(ExcelFiles) <- ExcelFilesNames
ExcelFilesList <- lapply(ExcelFilesNames,function(x) read.csv(ExcelFiles[x],sep = ';',dec = ',') )
names(ExcelFilesList) <- ExcelFilesNames
OutNames <- c("CCND1","EGFR variants I-IV ","EGFR variant I","EGFR variant IV","G0G1","GAPDH","MMP2","proliferation48H","BIRC5")
names(OutNames) <- ExcelFilesNames
# PlotsPaper mRNA -------------------------------------
mRNANames <- c("EGFRall","EGFRv1","EGFRv4","GAPDH","MMP2","SVV","CCND1")
PData <- data.frame()
for(mRNA in mRNANames){
mat <- matrix(as.double(unlist(ExcelFilesList[[mRNA]][,c(5:8)])),4,4)
dimnames(mat) <- list(as.character(ExcelFilesList[[mRNA]][,'Behandlung']),c('rep1','rep2','rep3','rep4'))
# rownames(mat)[2] <- 'siRNA-luci'
rownames(mat)[2] <- 'siRNA-nonsense'
print(mat)
pdata <- data.frame( "Treat" = rownames(mat),
"Gene" = mRNA,
"Out" = OutNames[mRNA],
"GeoMean" = (2^rowMeans(log2(mat),na.rm = T )),
"Mean" = rowMeans(mat,na.rm = T),
"stderr" = apply(mat,1,function(row) plotrix::std.error((row),na.rm = T)),
"sd" = apply(mat,1,function(row) sd((row),na.rm = T)))
PData <- rbind(PData,pdata)
rns <- levels(pdata$Treat)[c(1,4,2,3)] ; pdata$Treat <- factor(pdata$Treat, levels = rns)
limitsMeanErr <- aes(ymax = pdata$Mean + pdata$stderr , ymin=pdata$Mean - pdata$stderr) ; limitsMeanErrMAX <- round(max(pdata$Mean + pdata$stderr)+10)
limitsGeoMeanErr <- aes(ymax = pdata$GeoMean + pdata$stderr , ymin=pdata$GeoMean - pdata$stderr); limitsGeoMeanErrMAX <- round(max(pdata$GeoMean + pdata$stderr)+10)
limitsMeanSd <- aes(ymax = pdata$Mean + pdata$sd , ymin=pdata$Mean - pdata$sd); limitsMeanSdMAX <- round(max(pdata$Mean + pdata$sd)+10)
limitsGeoMeanSd <- aes(ymax = pdata$GeoMean + pdata$sd , ymin=pdata$GeoMean - pdata$sd); limitsGeoMeanSdMAX <- round(max(pdata$GeoMean + pdata$sd)+10)
PlotMeanSd <- ggplot(pdata,aes(x=factor(Treat),y=Mean)) +
geom_bar(position="dodge",stat="identity") +
geom_errorbar(limitsMeanSd, position=dodge, width=0.4) +
#ylim(c(0 ,limitsMeanSdMAX)) +
labs(x = "", y = paste0("relative mRNA level of ",OutNames[mRNA]," copies / \n HPRT copies"))+ # ,title="PAPER PLOT - Comparison of SF767 - Illumina data and RT-qPCR") +
thememapBarplot(base_size = base_size,legend_key_size = 0.6) +
scale_y_continuous(breaks=seq(0,limitsMeanSdMAX,20))
#theme(legend.position="bottom")
# scale_fill_manual(values = cols,name="") +
print(PlotMeanSd)
ggsave(plot = PlotMeanSd,filename = paste0(plotOUT,'mRNA_',mRNA,'_MeanSd.pdf'),width = 12,height = 8)
# PlotMeanErr <- ggplot(pdata,aes(x=factor(Treat),y=Mean)) +
# geom_bar(position="dodge",stat="identity") +
# geom_errorbar(limitsMeanErr, position=dodge, width=0.4) +
# # ylim(c(0 ,limitsMeanErrMAX)) +
# labs(x = "", y = paste0("relative mRNA level of ",OutNames[mRNA]," copies / \n HPRT copies"))+ # ,title="PAPER PLOT - Comparison of SF767 - Illumina data and RT-qPCR") +
# thememapBarplot(base_size = base_size,legend_key_size = 0.6) +
# scale_y_continuous(breaks=seq(0,limitsMeanErrMAX,20))
# #theme(legend.position="bottom")
# # scale_fill_manual(values = cols,name="") +
# print(PlotMeanErr)
# ggsave(plot = PlotMeanErr,filename = paste0(plotOUT,'mRNA_',mRNA,'_MeanErr.pdf'),width = 12,height = 8)
#
# PlotGeoMeanSd <- ggplot(pdata,aes(x=factor(Treat),y=GeoMean)) +
# geom_bar(position="dodge",stat="identity") +
# geom_errorbar(limitsGeoMeanSd, position=dodge, width=0.4) +
# #ylim(c(0 ,limitsGeoMeanSdMAX)) +
# labs(x = "", y = paste0("relative mRNA level of ",OutNames[mRNA]," copies / \n HPRT copies"))+ # ,title="PAPER PLOT - Comparison of SF767 - Illumina data and RT-qPCR") +
# thememapBarplot(base_size = base_size,legend_key_size = 0.6) +
# scale_y_continuous(breaks=seq(0,limitsGeoMeanSdMAX,20))
# #theme(legend.position="bottom")
# # scale_fill_manual(values = cols,name="") +
# print(PlotGeoMeanSd)
# ggsave(plot = PlotGeoMeanSd,filename = paste0(plotOUT,'mRNA_',mRNA,'_GeoMeanSd.pdf'),width = 12,height = 8)
#
# PlotGeoMeanErr <- ggplot(pdata,aes(x=factor(Treat),y=GeoMean)) +
# geom_bar(position="dodge",stat="identity") +
# geom_errorbar(limitsGeoMeanErr, position=dodge, width=0.4) +
# #ylim(c(0 ,limitsGeoMeanErrMAX)) +
# labs(x = "", y = paste0("relative mRNA level of ",OutNames[mRNA]," copies / \n HPRT copies"))+ # ,title="PAPER PLOT - Comparison of SF767 - Illumina data and RT-qPCR") +
# thememapBarplot(base_size = base_size,legend_key_size = 0.6) +
# scale_y_continuous(breaks=seq(0,limitsGeoMeanErrMAX,20))
# #theme(legend.position="bottom")
# # scale_fill_manual(values = cols,name="") +
# print(PlotGeoMeanErr)
# ggsave(plot = PlotGeoMeanErr,filename = paste0(plotOUT,'mRNA_',mRNA,'_GeoMeanErr.pdf'),width = 12,height = 8)
}
rns <- levels(PData$Treat)[c(1,4,2,3)] ; PData$Treat <- factor(PData$Treat, levels = rns)
limitsMeanErr <- aes(ymax = PData$Mean + PData$stderr , ymin=PData$Mean - PData$stderr) ; limitsMeanErrMAX <- round(max(PData$Mean + PData$stderr)+10)
limitsGeoMeanErr <- aes(ymax = PData$GeoMean + PData$stderr , ymin=PData$GeoMean - PData$stderr); limitsGeoMeanErrMAX <- round(max(PData$GeoMean + PData$stderr)+10)
limitsMeanSd <- aes(ymax = PData$Mean + PData$sd , ymin=PData$Mean - PData$sd); limitsMeanSdMAX <- round(max(PData$Mean + PData$sd)+10)
limitsGeoMeanSd <- aes(ymax = PData$GeoMean + PData$sd , ymin=PData$GeoMean - PData$sd); limitsGeoMeanSdMAX <- round(max(PData$GeoMean + PData$sd)+10)
PlotMeanSd <- ggplot(PData,aes(x=factor(Treat),y=Mean)) +
geom_bar(position="dodge",stat="identity") +
geom_errorbar(limitsMeanSd, position=dodge, width=0.4) +
#ylim(c(0 ,limitsMeanSdMAX)) +
labs(x = "", y = paste0("relative mRNA level of gene copies / HPRT copies")) +
thememapBarplot(base_size = base_size,legend_key_size = 0.6) +
scale_y_continuous(breaks=seq(0,limitsMeanSdMAX,20)) +
facet_grid(Out ~ .)
#theme(legend.position="bottom")
# scale_fill_manual(values = cols,name="") +
print(PlotMeanSd)
ggsave(plot = PlotMeanSd,filename = paste0(plotOUT,'mRNA_ALL_MeanSd.pdf'),width = 12,height = 20)
barCol <- RColorBrewer::brewer.pal(11,'Spectral')[6]
for(i in c("EGFR",'Control','Proli','EGFRControl')){
set <- switch(i,
"EGFR" = as.character( levels(PData$Gene)[1:3] ),
'Control' = as.character( levels(PData$Gene)[4:5] ),
'Proli' = as.character( levels(PData$Gene)[6:7] ),
'EGFRControl'= as.character( levels(PData$Gene)[1:5] )
)
tmp <- PData[PData$Gene %in% set,]
tmp_limitsMeanSd <- aes(ymax = tmp$Mean + tmp$sd , ymin=tmp$Mean - tmp$sd); limitsMeanSdMAX <- round(max(tmp$Mean + tmp$sd)+10)
PlotMeanSd <- ggplot(tmp,aes(x=factor(Treat),y=Mean)) +
geom_bar(position="dodge",stat="identity",fill=barCol,color='black') +
geom_errorbar(tmp_limitsMeanSd, position=dodge, width=0.4) +
labs(x = "", y = paste0("relative mRNA level of gene copies / HPRT copies")) +
thememapBarplot(base_size = base_size,legend_key_size = 0.6) +
scale_y_continuous(breaks=seq(0,limitsMeanSdMAX,20)) +
facet_grid(Out ~ .)
print(PlotMeanSd)
ggsave(plot = PlotMeanSd,filename = paste0(plotOUT,'mRNA_ALL_MeanSd_',i,'.pdf'),width = 8,height = 15)
}
# PlotMeanErr <- ggplot(PData,aes(x=factor(Treat),y=Mean)) +
# geom_bar(position="dodge",stat="identity") +
# geom_errorbar(limitsMeanErr, position=dodge, width=0.4) +
# # ylim(c(0 ,limitsMeanErrMAX)) +
# labs(x = "", y = paste0("relative mRNA level of gene copies / HPRT copies")) +
# thememapBarplot(base_size = base_size,legend_key_size = 0.6) +
# scale_y_continuous(breaks=seq(0,limitsMeanErrMAX,20)) +
# facet_grid(Out ~ .)
# #theme(legend.position="bottom")
# # scale_fill_manual(values = cols,name="") +
# print(PlotMeanErr)
# ggsave(plot = PlotMeanErr,filename = paste0(plotOUT,'mRNA_ALL_MeanErr.pdf'),width = 12,height = 20)
#
# PlotGeoMeanSd <- ggplot(PData,aes(x=factor(Treat),y=GeoMean)) +
# geom_bar(position="dodge",stat="identity") +
# geom_errorbar(limitsGeoMeanSd, position=dodge, width=0.4) +
# #ylim(c(0 ,limitsGeoMeanSdMAX)) +
# labs(x = "", y = paste0("relative mRNA level of gene copies / HPRT copies")) +
# thememapBarplot(base_size = base_size,legend_key_size = 0.6) +
# scale_y_continuous(breaks=seq(0,limitsGeoMeanSdMAX,20)) +
# facet_grid(Out ~ .)
# #theme(legend.position="bottom")
# # scale_fill_manual(values = cols,name="") +
# print(PlotGeoMeanSd)
# ggsave(plot = PlotGeoMeanSd,filename = paste0(plotOUT,'mRNA_ALL_GeoMeanSd.pdf'),width = 12,height = 20)
#
# PlotGeoMeanErr <- ggplot(PData,aes(x=factor(Treat),y=GeoMean)) +
# geom_bar(position="dodge",stat="identity") +
# geom_errorbar(limitsGeoMeanErr, position=dodge, width=0.4) +
# #ylim(c(0 ,limitsGeoMeanErrMAX)) +
# labs(x = "", y = paste0("relative mRNA level of gene copies / HPRT copies")) +
# thememapBarplot(base_size = base_size,legend_key_size = 0.6) +
# scale_y_continuous(breaks=seq(0,limitsGeoMeanErrMAX,20)) +
# facet_grid(Out ~ .)
# #theme(legend.position="bottom")
# # scale_fill_manual(values = cols,name="") +
# print(PlotGeoMeanErr)
# ggsave(plot = PlotGeoMeanErr,filename = paste0(plotOUT,'mRNA_ALL_GeoMeanErr.pdf'),width = 12,height = 20)
# PlotsPaper proliferation48H -------------------------------------
set <- "proliferation48H"
mat <- matrix(as.double(unlist(ExcelFilesList[[set]][,c(3:7)])),10,5)
dimnames(mat) <- list(as.character(ExcelFilesList[[set]][,'Behandlung']),c('rep1','rep2','rep3','rep4','rep5'))
mat <- mat[c("K","Lu","All",'Full'),]
# rownames(mat) <- c('control','siRNA-luci','siRNA-ALL','siRNA-I')
rownames(mat) <- c('control','siRNA-nonsense','siRNA-ALL','siRNA-I')
pdata <- data.frame( "Treat" = rownames(mat),
# "Out" = OutNames[mRNA],
"GeoMean" = (2^rowMeans(log2(mat),na.rm = T )),
"Mean" = rowMeans(mat,na.rm = T),
"stderr" = apply(mat,1,function(row) plotrix::std.error((row),na.rm = T)),
"sd" = apply(mat,1,function(row) sd((row),na.rm = T)))
rns <- levels(pdata$Treat)[c(1,4,2,3)] ; pdata$Treat <- factor(pdata$Treat, levels = rns)
limitsMeanErr <- aes(ymax = pdata$Mean + pdata$stderr , ymin=pdata$Mean - pdata$stderr) ; limitsMeanErrMAX <- round(max(pdata$Mean + pdata$stderr)+10)
limitsGeoMeanErr <- aes(ymax = pdata$GeoMean + pdata$stderr , ymin=pdata$GeoMean - pdata$stderr); limitsGeoMeanErrMAX <- round(max(pdata$GeoMean + pdata$stderr)+10)
limitsMeanSd <- aes(ymax = pdata$Mean + pdata$sd , ymin=pdata$Mean - pdata$sd); limitsMeanSdMAX <- round(max(pdata$Mean + pdata$sd)+10)
limitsGeoMeanSd <- aes(ymax = pdata$GeoMean + pdata$sd , ymin=pdata$GeoMean - pdata$sd); limitsGeoMeanSdMAX <- round(max(pdata$GeoMean + pdata$sd)+10)
PlotMeanSd <- ggplot(pdata,aes(x=factor(Treat),y=Mean)) +
geom_bar(position="dodge",stat="identity",fill=barCol,color='black') +
geom_errorbar(limitsMeanSd, position=dodge, width=0.4) +
ylim(c(0 ,limitsMeanSdMAX)) +
# scale_y_continuous(breaks=seq(0,limitsMeanSdMAX,20)) +
labs(x = "", y = paste0("cell number after 48 hours \n of proliferation"))+ # ,title="PAPER PLOT - Comparison of SF767 - Illumina data and RT-qPCR") +
thememapBarplot(base_size = base_size,legend_key_size = 0.6)
#theme(legend.position="bottom")
# scale_fill_manual(values = cols,name="") +
print(PlotMeanSd)
ggsave(plot = PlotMeanSd,filename = paste0(plotOUT,'proliferation48H_MeanSd.pdf'),width = 12,height = 8)
# PlotMeanErr <- ggplot(pdata,aes(x=factor(Treat),y=Mean)) +
# geom_bar(position="dodge",stat="identity") +
# geom_errorbar(limitsMeanErr, position=dodge, width=0.4) +
# ylim(c(0 ,limitsMeanErrMAX)) +
# labs(x = "", y = paste0("cell number after 48 hours of proliferation"))+ # ,title="PAPER PLOT - Comparison of SF767 - Illumina data and RT-qPCR") +
# thememapBarplot(base_size = base_size,legend_key_size = 0.6)
# #theme(legend.position="bottom")
# # scale_fill_manual(values = cols,name="") +
# print(PlotMeanErr)
# ggsave(plot = PlotMeanErr,filename = paste0(plotOUT,'proliferation48H_MeanErr.pdf'),width = 12,height = 8)
#
# PlotGeoMeanSd <- ggplot(pdata,aes(x=factor(Treat),y=GeoMean)) +
# geom_bar(position="dodge",stat="identity") +
# geom_errorbar(limitsGeoMeanSd, position=dodge, width=0.4) +
# ylim(c(0 ,limitsGeoMeanSdMAX)) +
# labs(x = "", y = paste0("cell number after 48 hours of proliferation"))+ # ,title="PAPER PLOT - Comparison of SF767 - Illumina data and RT-qPCR") +
# thememapBarplot(base_size = base_size,legend_key_size = 0.6)
# #theme(legend.position="bottom")
# # scale_fill_manual(values = cols,name="") +
# print(PlotGeoMeanSd)
# ggsave(plot = PlotGeoMeanSd,filename = paste0(plotOUT,'proliferation48H_GeoMeanSd.pdf'),width = 12,height = 8)
#
# PlotGeoMeanErr <- ggplot(pdata,aes(x=factor(Treat),y=GeoMean)) +
# geom_bar(position="dodge",stat="identity") +
# geom_errorbar(limitsGeoMeanErr, position=dodge, width=0.4) +
# ylim(c(0 ,limitsGeoMeanErrMAX)) +
# labs(x = "", y = paste0("cell number after 48 hours of proliferation"))+ # ,title="PAPER PLOT - Comparison of SF767 - Illumina data and RT-qPCR") +
# thememapBarplot(base_size = base_size,legend_key_size = 0.6)
# #theme(legend.position="bottom")
# # scale_fill_manual(values = cols,name="") +
# print(PlotGeoMeanErr)
# ggsave(plot = PlotGeoMeanErr,filename = paste0(plotOUT,'proliferation48H_GeoMeanErr.pdf'),width = 12,height = 8)
# PlotsPaper G0G1 -------------------------------------
set <- "G0G1"
mat <- matrix(as.double(unlist(ExcelFilesList[[set]][,c(3:8)])),10,6)
dimnames(mat) <- list(as.character(ExcelFilesList[[set]][,'Bezeichnung']),c('rep1','rep2','rep3','rep4','rep5','rep6'))
mat <- mat[c("Ko","LU","All",'Full'),]
rownames(mat) <- c('control','siRNA-luci','siRNA-ALL','siRNA-I')
rownames(mat) <- c('control','siRNA-nonsense','siRNA-ALL','siRNA-I')
pdata <- data.frame( "Treat" = rownames(mat),
# "Out" = OutNames[mRNA],
"GeoMean" = (2^rowMeans(log2(mat),na.rm = T )),
"Mean" = rowMeans(mat,na.rm = T),
"stderr" = apply(mat,1,function(row) plotrix::std.error((row),na.rm = T)),
"sd" = apply(mat,1,function(row) sd((row),na.rm = T)))
rns <- levels(pdata$Treat)[c(1,4,2,3)] ; pdata$Treat <- factor(pdata$Treat, levels = rns)
limitsMeanErr <- aes(ymax = pdata$Mean + pdata$stderr , ymin=pdata$Mean - pdata$stderr) ; limitsMeanErrMAX <- round(max(pdata$Mean + pdata$stderr)+10)
limitsGeoMeanErr <- aes(ymax = pdata$GeoMean + pdata$stderr , ymin=pdata$GeoMean - pdata$stderr); limitsGeoMeanErrMAX <- round(max(pdata$GeoMean + pdata$stderr)+10)
limitsMeanSd <- aes(ymax = pdata$Mean + pdata$sd , ymin=pdata$Mean - pdata$sd); limitsMeanSdMAX <- round(max(pdata$Mean + pdata$sd)+10)
limitsGeoMeanSd <- aes(ymax = pdata$GeoMean + pdata$sd , ymin=pdata$GeoMean - pdata$sd); limitsGeoMeanSdMAX <- round(max(pdata$GeoMean + pdata$sd)+10)
PlotMeanSd <- ggplot(pdata,aes(x=factor(Treat),y=Mean)) +
geom_bar(position="dodge",stat="identity",fill=barCol,color='black') +
geom_errorbar(limitsMeanSd, position=dodge, width=0.4) +
# ylim(c(0 ,limitsMeanSdMAX)) +
scale_y_continuous(breaks=seq(0,limitsMeanSdMAX,10)) +
labs(x = "", y = paste0("relative level of G0/G1 cells"))+ # ,title="PAPER PLOT - Comparison of SF767 - Illumina data and RT-qPCR") +
thememapBarplot(base_size = base_size,legend_key_size = 0.6)
print(PlotMeanSd)
ggsave(plot = PlotMeanSd,filename = paste0(plotOUT,'G0G1_MeanSd.pdf'),width = 12,height = 8)
# PlotMeanErr <- ggplot(pdata,aes(x=factor(Treat),y=Mean)) +
# geom_bar(position="dodge",stat="identity") +
# geom_errorbar(limitsMeanErr, position=dodge, width=0.4) +
# scale_y_continuous(breaks=seq(0,limitsMeanErrMAX,10)) +
# labs(x = "", y = paste0("relative level of G0/G1 cells"))+ # ,title="PAPER PLOT - Comparison of SF767 - Illumina data and RT-qPCR") +
# thememapBarplot(base_size = base_size,legend_key_size = 0.6)
# #theme(legend.position="bottom")
# # scale_fill_manual(values = cols,name="") +
# print(PlotMeanErr)
# ggsave(plot = PlotMeanErr,filename = paste0(plotOUT,'G0G1_MeanErr.pdf'),width = 12,height = 8)
#
# PlotGeoMeanSd <- ggplot(pdata,aes(x=factor(Treat),y=GeoMean)) +
# geom_bar(position="dodge",stat="identity") +
# geom_errorbar(limitsGeoMeanSd, position=dodge, width=0.4) +
# scale_y_continuous(breaks=seq(0,limitsGeoMeanSdMAX,10)) +
# labs(x = "", y = paste0("relative level of G0/G1 cells"))+ # ,title="PAPER PLOT - Comparison of SF767 - Illumina data and RT-qPCR") +
# thememapBarplot(base_size = base_size,legend_key_size = 0.6)
# #theme(legend.position="bottom")
# # scale_fill_manual(values = cols,name="") +
# print(PlotGeoMeanSd)
# ggsave(plot = PlotGeoMeanSd,filename = paste0(plotOUT,'G0G1_GeoMeanSd.pdf'),width = 12,height = 8)
#
# PlotGeoMeanErr <- ggplot(pdata,aes(x=factor(Treat),y=GeoMean)) +
# geom_bar(position="dodge",stat="identity") +
# geom_errorbar(limitsGeoMeanErr, position=dodge, width=0.4) +
# scale_y_continuous(breaks=seq(0,limitsGeoMeanErrMAX,10)) +
# labs(x = "", y = paste0("relative level of G0/G1 cells"))+ # ,title="PAPER PLOT - Comparison of SF767 - Illumina data and RT-qPCR") +
# thememapBarplot(base_size = base_size,legend_key_size = 0.6)
# #theme(legend.position="bottom")
# # scale_fill_manual(values = cols,name="") +
# print(PlotGeoMeanErr)
# ggsave(plot = PlotGeoMeanErr,filename = paste0(plotOUT,'G0G1_GeoMeanErr.pdf'),width = 12,height = 8)
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